The use of Electric Vehicle (EV) for transportation has increased, over the past years. The vehicle’s autonomy in terms of its Driving Range (DR) capability is a key factor for EV assessment. However, the autonomy of the vehicle depends on many variables related to vehicle itself as well as on external conditions. This makes challenging to provide and accurate estimation of the DR value, at each moment. In this paper, we address the use of Machine Learning (ML) regression techniques to estimate the DR. In detail, we evaluate the use of Multilayer Perceptron (MLP) and Radial
Basis Function (RBF) neural networks to the DR estimation problem. The neural network approaches attain adequate results, with standard metrics, showing room for improvement.